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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "###Import Libraries"
      ],
      "metadata": {
        "id": "JRsLSJnT70EJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YnVeVHNA7Ujf"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import ipywidgets\n",
        "from ipywidgets import interact\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "LHP_CeXH75yu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_excel(\"data.xlsx\")\n"
      ],
      "metadata": {
        "id": "1zvk9z1X76Nj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "data.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "HX9LDye476l6",
        "outputId": "639e899f-70a0-4183-9410-02522241074f"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                 N            P            K  temperature     humidity  \\\n",
              "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
              "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
              "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
              "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
              "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
              "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
              "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
              "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
              "\n",
              "                ph     rainfall  \n",
              "count  2200.000000  2200.000000  \n",
              "mean      6.469480   103.463655  \n",
              "std       0.773938    54.958389  \n",
              "min       3.504752    20.211267  \n",
              "25%       5.971693    64.551686  \n",
              "50%       6.425045    94.867624  \n",
              "75%       6.923643   124.267508  \n",
              "max       9.935091   298.560117  "
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              "      <th>N</th>\n",
              "      <th>P</th>\n",
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              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>50.551818</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
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              "      <td>5.000000</td>\n",
              "      <td>8.825675</td>\n",
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              "      <td>3.504752</td>\n",
              "      <td>20.211267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>21.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>22.769375</td>\n",
              "      <td>60.261953</td>\n",
              "      <td>5.971693</td>\n",
              "      <td>64.551686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>51.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>25.598693</td>\n",
              "      <td>80.473146</td>\n",
              "      <td>6.425045</td>\n",
              "      <td>94.867624</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>84.250000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>28.561654</td>\n",
              "      <td>89.948771</td>\n",
              "      <td>6.923643</td>\n",
              "      <td>124.267508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>140.000000</td>\n",
              "      <td>145.000000</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>43.675493</td>\n",
              "      <td>99.981876</td>\n",
              "      <td>9.935091</td>\n",
              "      <td>298.560117</td>\n",
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              "  </tbody>\n",
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              "          const element = document.querySelector('#df-39ccb140-868a-4070-a8a9-d37929415dcc');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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    {
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      "source": [
        "data[\"label\"].value_counts()"
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        "colab": {
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        "id": "teqaKqPM9QeR",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rice           100\n",
              "maize          100\n",
              "jute           100\n",
              "cotton         100\n",
              "coconut        100\n",
              "papaya         100\n",
              "orange         100\n",
              "apple          100\n",
              "muskmelon      100\n",
              "watermelon     100\n",
              "grapes         100\n",
              "mango          100\n",
              "banana         100\n",
              "pomegranate    100\n",
              "lentil         100\n",
              "blackgram      100\n",
              "mungbean       100\n",
              "mothbeans      100\n",
              "pigeonpeas     100\n",
              "kidneybeans    100\n",
              "chickpea       100\n",
              "coffee         100\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2200, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
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          "data": {
            "text/plain": [
              "    N   P   K  temperature   humidity        ph    rainfall label\n",
              "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
              "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
              "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
              "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
              "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "          document.querySelector('#df-c19b4d78-dc8d-4c56-bda3-da44dcfab1e6 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-c19b4d78-dc8d-4c56-bda3-da44dcfab1e6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
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        "id": "k3gS67K69F4B",
        "outputId": "ef39e843-17f2-4c1f-ad17-757baad33c00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N              0\n",
              "P              0\n",
              "K              0\n",
              "temperature    0\n",
              "humidity       0\n",
              "ph             0\n",
              "rainfall       0\n",
              "label          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Analytics"
      ],
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        "id": "mXKpsBNOCtSe"
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          "output_type": "execute_result",
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              "50.551818181818184"
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              "53.36272727272727"
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              "6.469480065256362"
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          "metadata": {},
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      "cell_type": "code",
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        "data['rainfall'].mean()"
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              "103.46365541576816"
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          "metadata": {},
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        "data['humidity'].mean()"
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              "71.48177921778637"
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          "metadata": {},
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        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "data['temperature'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXrNBmbZDQPE",
        "outputId": "2e7a3a51-249a-4a15-c27a-0f0d9f0dfa8b"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.616243851779544"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "@interact\n",
        "def summary(crops = list(data['label'].value_counts().index)):\n",
        "    x = data[data['label'] == crops]\n",
        "    print(\"---------------------------------------------\")\n",
        "    print(\"Nitrogen\")\n",
        "    print(\" Min Nitrigen   :\", x['N'].min())\n",
        "    print(\"Avg Nitrogen   :\", x['N'].mean())\n",
        "    print(\"Max Nitrogen   :\", x['N'].max())\n",
        "    print(\"---------------------------------------------\")\n",
        "    print(\"Phosphorous\")\n",
        "    print(\" Min Phosphorous   :\", x['P'].min())\n",
        "    print(\"Avg Phosphorous   :\", x['P'].mean())\n",
        "    print(\"Max Phosphorous   :\", x['P'].max())\n",
        "    print(\"---------------------------------------------\")\n",
        "    print(\"Potassium\")\n",
        "    print(\" Min Potassium :\", x['K'].min())\n",
        "    print(\"Avg Potassium :\", x['K'].mean())\n",
        "    print(\"Max Potassium :\", x['K'].max())\n",
        "    print(\"---------------------------------------------\")\n",
        "    print(\"Temperature\")\n",
        "    print(\" Min Temp : {}\".format(x['temperature'].min()))\n",
        "    print(\"Avg Temp : {}\".format(x['temperature'].mean()))\n",
        "    print(\"Max Temp : {}\".format(x['temperature'].max()))\n",
        "    print(\"---------------------------------------------\")\n",
        "    print(\"Statistics for Humidity\")\n",
        "    print(\" Min Humidity   : {}\".format(x['humidity'].min()))\n",
        "    print(\"Avg Humidity   : {}\".format(x['humidity'].mean()))\n",
        "    print(\"Max Humidity   : {}\".format(x['humidity'].max()))\n",
        "    print(\"---------------------------------------------\")\n",
        "    print(\"PH\")\n",
        "    print(\" Min PH   : {0:.2f}\".format(x['ph'].min()))\n",
        "    print(\"Avg PH   : {0:.2f}\".format(x['ph'].mean()))\n",
        "    print(\"Max PH   : {0:.2f}\".format(x['ph'].max()))\n",
        "    print(\"---------------------------------------------\")\n",
        "    print(\"Rainfall\")\n",
        "    print(\" Min Rainfall   : {0:.2f}\".format(x['rainfall'].min()))\n",
        "    print(\"Avg Rainfall   : {0:.2f}\".format(x['rainfall'].mean()))\n",
        "    print(\"Max Rainfall   : {0:.2f}\".format(x['rainfall'].max()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656,
          "referenced_widgets": [
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        "outputId": "4c677415-fde4-4f30-8849-b6683f9d0379"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='crops', options=('rice', 'maize', 'jute', 'cotton', 'coconut', 'pa…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7d913d205284ddba9a3a0d44a51a730"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "@interact\n",
        "def compare(conditions=[\"N\",\"P\",\"K\",\"temperature\",\"ph\",\"humidity\",\"rainfall\"]):\n",
        "  print(\"Low Sustainable Crops\\n\")\n",
        "  print(data[data[conditions]> data[conditions].mean()]['label'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
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            "ec7aa2041e28430893b16990f0c320d3",
            "e6cd1c321ed14ca38c0e3b48878f9e70"
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        },
        "id": "7gD-z9qIFJ-u",
        "outputId": "b61b787e-01a3-435f-d60e-f3fccca94dad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='conditions', options=('N', 'P', 'K', 'temperature', 'ph', 'humidit…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f376811a3a10479d8aaabf5a7eb60f41"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "@interact\n",
        "def compare(conditions=[\"N\",\"P\",\"K\",\"temperature\",\"ph\",\"humidity\",\"rainfall\"]):\n",
        "  print(\"High Sustainable Crops\\n\")\n",
        "  print(data[data[conditions]< data[conditions].mean()]['label'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "9af1b8a830524007a446e647e9f61ff9",
            "df1a16bd0b3c456f91c776531b040024",
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        "id": "Svt4jkkJHJzU",
        "outputId": "35673a3b-0cf9-4bf6-d5bc-ef065dfde55d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='conditions', options=('N', 'P', 'K', 'temperature', 'ph', 'humidit…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9af1b8a830524007a446e647e9f61ff9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,1,1)\n",
        "sns.histplot(data['N'],color=\"blue\")\n",
        "plt.title(\"Nitrogen\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "v1ZLVeG0Ha20",
        "outputId": "26d75c3c-3182-4591-926c-0a5fcc541e0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,1,1)\n",
        "sns.histplot(data['P'],color=\"red\")\n",
        "plt.title(\"Phosphorus\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "AN7nke8yIr5u",
        "outputId": "c015f5dd-b5d8-475a-a089-5eb910d54a23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,1,1)\n",
        "sns.histplot(data['K'],color=\"yellow\")\n",
        "plt.title(\"Potassium\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "2XzhMVmRIyQg",
        "outputId": "15ce3fdb-b137-4847-c347-d57efd8a06a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,1,1)\n",
        "sns.histplot(data['temperature'],color=\"orange\")\n",
        "plt.title(\"Temperature(C)\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "29ML9O-PJAZG",
        "outputId": "ab0a23f1-c711-4045-f99f-70371d7baa5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,1,1)\n",
        "sns.histplot(data['ph'],color=\"pink\")\n",
        "plt.title(\"ph\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "pES_rGcuJLmM",
        "outputId": "309ded11-53a0-46cd-ba76-83fde8e3d35e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,1,1)\n",
        "sns.histplot(data['humidity'],color=\"green\")\n",
        "plt.title(\"Humidity\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ABsQof-gJRfw",
        "outputId": "9e98f6d2-ff6a-47b9-83f4-51fc9efd0f90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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jFPyMPkZms1l2u10Ol0MOOXxa2+FyyG63y2w2+/Uc+mOMWlsv6O6z43K5vObTnKuoqEiS1LNnT0lSamqqDhw4oPLyck+bvLw82Ww2DRgwwO99BQAAwS+gV3ays7M1duxYJSYm6vTp08rNzdX27du1adMmff755575O927d9f+/fu1YMECpaWlafDgwZKk9PR0DRgwQNOmTdPy5ctVWlqqRYsWKTMzs9krNwAA4PIT0LBTXl6u6dOn68SJE4qMjNTgwYO1adMmjRkzRseOHdOWLVu0YsUKnT17VgkJCZo0aZIWLVrk2b5Tp07asGGD5syZo9TUVHXp0kUzZszwui8PAAC4vAU07Lz88sstrktISNCOHTsuWKN379569913fdktAABgIEE3ZwcAAMCXCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQCDsAAMDQAhp2Vq1apcGDB8tms8lmsyk1NVUbN270rK+trVVmZqa6d++url27atKkSSorK/OqUVJSonHjxik8PFwxMTF64IEHVF9f396HAgAAglRAw06vXr20bNkyFRYW6sMPP9Stt96qO+64QwcPHpQkLViwQO+8847WrVunHTt26Pjx45o4caJn+4aGBo0bN04Oh0M7d+7UK6+8ojVr1ujhhx8O1CEBAIAg0zmQOx8/frzX88cff1yrVq3Srl271KtXL7388svKzc3VrbfeKklavXq1+vfvr127dmnkyJHavHmzDh06pC1btig2NlYpKSl69NFHtXDhQuXk5CgkJCQQhwUAAIJIQMPOuRoaGrRu3TqdPXtWqampKiwslNPp1OjRoz1t+vXrp8TERBUUFGjkyJEqKCjQoEGDFBsb62mTkZGhOXPm6ODBgxo6dGiz+6qrq1NdXZ3neXV1tSTJ6XTK6XT66QjbT+MxGOFYjIxxCn6MUfC7XMbI5XLJarUqxByiEPn2D/kQc4isVqtcLpdfzqM/x6i1NQMedg4cOKDU1FTV1taqa9eueuuttzRgwAAVFRUpJCREUVFRXu1jY2NVWloqSSotLfUKOo3rG9e1ZOnSpVq8eHGT5Zs3b1Z4ePglHlHwyMvLC3QX0AqMU/BjjILf5TBGa9eu9U/hKElrpa+//lpff/21f/Yh/4xRTU1Nq9oFPOwkJyerqKhIVVVVevPNNzVjxgzt2LHDr/vMzs5WVlaW53l1dbUSEhKUnp4um83m1323B6fTqby8PI0ZM0YWiyXQ3UELGKfgxxgFv8tljPbt26e0tDSlZKcoIjHCp7VPl5xW0dIi5efna8iQIT6tLfl3jBpfmbmQgIedkJAQXXXVVZKkYcOGae/evfrjH/+on/zkJ3I4HKqsrPS6ulNWVqa4uDhJUlxcnPbs2eNVr/HdWo1tmhMaGqrQ0NAmyy0Wi6G+WYx2PEbFOAU/xij4GX2MzGaz7Ha7HC6HHHL4tLbD5ZDdbpfZbPbrOfTHGLW2XtDdZ8flcqmurk7Dhg2TxWLR1q1bPeuKi4tVUlKi1NRUSVJqaqoOHDig8vJyT5u8vDzZbDYNGDCg3fsOAACCT0Cv7GRnZ2vs2LFKTEzU6dOnlZubq+3bt2vTpk2KjIzUzJkzlZWVpejoaNlsNs2bN0+pqakaOXKkJCk9PV0DBgzQtGnTtHz5cpWWlmrRokXKzMxs9soNAAC4/AQ07JSXl2v69Ok6ceKEIiMjNXjwYG3atEljxoyRJD399NMym82aNGmS6urqlJGRoeeee86zfadOnbRhwwbNmTNHqamp6tKli2bMmKElS5YE6pAAAECQCWjYefnll8+7PiwsTCtXrtTKlStbbNO7d2+9++67vu4aAAAwiKCbswMAAOBLhB0AAGBohB0AAGBohB0AAGBohB0AAGBoAb+DMgAA7aGkpEQVFRV+q9+jRw8lJib6rT7ajrADADC8kpISJfdLVq291m/7CLOGqfiTYgJPECLsAAAMr6KiQrX2WvWf3V/h8eE+r19zvEaHXzysiooKwk4QIuwAAC4b4fHhiujj208NR/BjgjIAADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADA0wg4AADC0gIadpUuX6rrrrlNERIRiYmI0YcIEFRcXe7W5+eabZTKZvB4///nPvdqUlJRo3LhxCg8PV0xMjB544AHV19e356EAAIAg1TmQO9+xY4cyMzN13XXXqb6+Xr/5zW+Unp6uQ4cOqUuXLp52s2bN0pIlSzzPw8PDPf9vaGjQuHHjFBcXp507d+rEiROaPn26LBaLnnjiiXY9HgAAEHwCGnbee+89r+dr1qxRTEyMCgsLlZaW5lkeHh6uuLi4Zmts3rxZhw4d0pYtWxQbG6uUlBQ9+uijWrhwoXJychQSEuLXYwAAAMEtqObsVFVVSZKio6O9lr/22mvq0aOHBg4cqOzsbNXU1HjWFRQUaNCgQYqNjfUsy8jIUHV1tQ4ePNg+HQcAAEEroFd2zuVyuTR//nzdcMMNGjhwoGf55MmT1bt3b8XHx2v//v1auHChiouLtX79eklSaWmpV9CR5HleWlra7L7q6upUV1fneV5dXS1JcjqdcjqdPj2uQGg8BiMci5ExTsGPMQp+rR0jl8slq9WqEHOIQuT7K/4h5hBZrVa5XC6/fL34s//+7rs/v49aW9PkdrvdPt97G8yZM0cbN27UBx98oF69erXYbtu2bRo1apSOHDmiK6+8UrNnz9aXX36pTZs2edrU1NSoS5cuevfddzV27NgmNXJycrR48eImy3Nzc73mAwEAgOBVU1OjyZMnq6qqSjabrcV2QXFlZ+7cudqwYYPy8/PPG3QkacSIEZLkCTtxcXHas2ePV5uysjJJanGeT3Z2trKysjzPq6urlZCQoPT09POerI7C6XQqLy9PY8aMkcViCXR30ALGKfgxRsGvtWO0b98+paWlKSU7RRGJET7vx+mS0ypaWqT8/HwNGTLE5/X92X9/992f30eNr8xcSEDDjtvt1rx58/TWW29p+/btSkpKuuA2RUVFkqSePXtKklJTU/X444+rvLxcMTExkqS8vDzZbDYNGDCg2RqhoaEKDQ1tstxisRjqB5rRjseoGKfgxxgFvwuNkdlslt1ul8PlkEMOn+/f4XLIbreruLhYZrPvp8MWFxf7rf+NfTebzX79OvfH91Fr6wU07GRmZio3N1d//etfFRER4ZljExkZKavVqs8//1y5ubm6/fbb1b17d+3fv18LFixQWlqaBg8eLElKT0/XgAEDNG3aNC1fvlylpaVatGiRMjMzmw00AAD4mqPSIZmkqVOn+nc/Dt8HtctBQMPOqlWrJH1z48BzrV69Wvfcc49CQkK0ZcsWrVixQmfPnlVCQoImTZqkRYsWedp26tRJGzZs0Jw5c5SamqouXbpoxowZXvflAQDAn+pr6iW31Gd6H3Xv293n9U/uP6mj649yw9w2CvjLWOeTkJCgHTt2XLBO79699e677/qqWwAAtIk1zqqIPr6fE1RzvObCjdCioJigDABASUmJKioqLmobl8sl6ZsJvOebK3P48OFL6hs6NsIOACDgSkpKlNwvWbX22ovazmq1au3atUpLS5Pdbr9ge+a8XJ4IOwCAgKuoqFCtvVb9Z/dXeHzr73cWYv7mBnsp2SlyuFoOMsx5ubwRdgAAQSM8Pvyi5rw03k04IjHivG/JZs7L5S2oPhsLAADA1wg7AADA0Ag7AADA0Ag7AADA0Ag7AADA0Ag7AADA0Ag7AADA0Ag7AADA0NoUdvr27auTJ082WV5ZWam+fftecqcAAAB8pU1h5+jRo2poaGiyvK6uTl9//fUldwoAAMBXLurjIv72t795/r9p0yZFRkZ6njc0NGjr1q3q06ePzzoHAABwqS4q7EyYMEGSZDKZNGPGDK91FotFffr00R/+8AefdQ4AAOBSXVTYcblckqSkpCTt3btXPXr08EunAAAAfKVNn3r+xRdf+LofAAAAftGmsCNJW7du1datW1VeXu654tPoz3/+8yV3DAAAwBfaFHYWL16sJUuWaPjw4erZs6dMJpOv+wUAAOATbQo7zz//vNasWaNp06b5uj8AAAA+1ab77DgcDn3ve9/zdV8AAAB8rk1h57777lNubq6v+wIAAOBzbXoZq7a2Vi+++KK2bNmiwYMHy2KxeK1/6qmnfNI5AACAS9WmsLN//36lpKRIkj7++GOvdUxWBgAAwaRNYef999/3dT8AAAD8ok1zdgAAADqKNl3ZueWWW877ctW2bdva3CEAAABfalPYaZyv08jpdKqoqEgff/xxkw8IBQAACKQ2hZ2nn3662eU5OTk6c+bMJXUIAADAl3w6Z2fq1Kl8LhYAAAgqPg07BQUFCgsL82VJAACAS9Kml7EmTpzo9dztduvEiRP68MMP9dBDD/mkYwAAAL7QprATGRnp9dxsNis5OVlLlixRenq6TzoGAADgC20KO6tXr/bJzpcuXar169frk08+kdVq1fe+9z09+eSTSk5O9rSpra3Vr371K73++uuqq6tTRkaGnnvuOcXGxnralJSUaM6cOXr//ffVtWtXzZgxQ0uXLlXnzm06PAAAYCCXNGensLBQr776ql599VV99NFHF739jh07lJmZqV27dikvL09Op1Pp6ek6e/asp82CBQv0zjvvaN26ddqxY4eOHz/u9TJaQ0ODxo0bJ4fDoZ07d+qVV17RmjVr9PDDD1/KoQEAAINo06WP8vJy3XXXXdq+fbuioqIkSZWVlbrlllv0+uuv64orrmhVnffee8/r+Zo1axQTE6PCwkKlpaWpqqpKL7/8snJzc3XrrbdK+uaqUv/+/bVr1y6NHDlSmzdv1qFDh7RlyxbFxsYqJSVFjz76qBYuXKicnByFhIS05RABAIBBtCnszJs3T6dPn9bBgwfVv39/SdKhQ4c0Y8YM/fKXv9TatWvb1JmqqipJUnR0tKRvrhw5nU6NHj3a06Zfv35KTExUQUGBRo4cqYKCAg0aNMjrZa2MjAzNmTNHBw8e1NChQ5vsp66uTnV1dZ7n1dXVkr65OaLT6WxT34NJ4zEY4ViMjHEKfoxR+3G5XLJarQoxhyhErf8j1SKL178tCe0cKqvVqtBOoRdVv7U6cv0Qc4isVqtcLpdfvtb9+X3U2pomt9vtvtjikZGR2rJli6677jqv5Xv27FF6eroqKysvtqRcLpd++MMfqrKyUh988IEkKTc3V/fee69XMJGk66+/XrfccouefPJJzZ49W19++aU2bdrkWV9TU6MuXbro3Xff1dixY5vsKycnR4sXL26yPDc3V+Hh4RfddwAA0P5qamo0efJkVVVVyWaztdiuTVd2XC6XLJamKdpiscjlcrWlpDIzM/Xxxx97go4/ZWdnKysry/O8urpaCQkJSk9PP+/J6iicTqfy8vI0ZsyYZscJwYFxCn6MUfvZt2+f0tLSlJKdoojEiFZvZ5FFs6Jm6aXKl+RUy3/ll+8pV/HqYiXPT1ZMcowvumyY+qdLTqtoaZHy8/M1ZMgQn9aW/Pt91PjKzIW0Kezceuutuv/++7V27VrFx8dLkr7++mstWLBAo0aNuuh6c+fO1YYNG5Sfn69evXp5lsfFxcnhcKiystIzN0iSysrKFBcX52mzZ88er3plZWWedc0JDQ1VaGhok+UWi8VQP9CMdjxGxTgFP8bI/8xms+x2uxwuhxxyXPT2TjnPu11dfZ3sdrvqGuraVP9COnJ9h8shu90us9ns169zf3wftbZem96N9ac//UnV1dXq06ePrrzySl155ZVKSkpSdXW1nn322VbXcbvdmjt3rt566y1t27ZNSUlJXuuHDRsmi8WirVu3epYVFxerpKREqampkqTU1FQdOHBA5eXlnjZ5eXmy2WwaMGBAWw4PAAAYSJuu7CQkJOif//yntmzZok8++USS1L9/f6+JxK2RmZmp3Nxc/fWvf1VERIRKS0slfTMnyGq1KjIyUjNnzlRWVpaio6Nls9k0b948paamauTIkZKk9PR0DRgwQNOmTdPy5ctVWlqqRYsWKTMzs9mrNwAA4PJyUVd2tm3bpgEDBqi6ulomk0ljxozRvHnzNG/ePF133XW69tpr9Y9//KPV9VatWqWqqirdfPPN6tmzp+fxxhtveNo8/fTT+sEPfqBJkyYpLS1NcXFxWr9+vWd9p06dtGHDBnXq1EmpqamaOnWqpk+friVLllzMoQEAAIO6qCs7K1as0KxZs5qdxBsZGamf/exneuqpp/T973+/VfVa80awsLAwrVy5UitXrmyxTe/evfXuu++2ap8AAODyclFXdvbt26fbbrutxfXp6ekqLCy85E4BAAD4ykWFnbKysvPOfO7cubP+/e9/X3KnAAAAfOWiws53vvMdffzxxy2u379/v3r27HnJnQIAAPCViwo7t99+ux566CHV1tY2WWe32/XII4/oBz/4gc86BwAAcKkuaoLyokWLtH79el1zzTWaO3eukpOTJUmffPKJVq5cqYaGBv32t7/1S0cBAADa4qLCTmxsrHbu3Kk5c+YoOzvb824qk8mkjIwMrVy50usDOQEAAALtom8q2Pg27//85z86cuSI3G63rr76anXr1s0f/QMAALgkbbqDsiR169atyaeeAwAABJs2fTYWAABAR0HYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhtY50B0AgGBTUlKiiooKuVwuSdK+fftkNvvmb8MePXooMTHRJ7UAtA5hBwDOUVJSouR+yaq118pqtWrt2rVKS0uT3W73Sf0wa5iKPykm8ADtiLADAOeoqKhQrb1W/Wf3V1SvKElSSnaKHC7HJdeuOV6jwy8eVkVFBWEHaEeEHQBoRnh8uCISIyRJEYkRcujSww6AwGCCMgAAMDTCDgAAMDTCDgAAMDTCDgAAMDTCDgAAMLSAhp38/HyNHz9e8fHxMplMevvtt73W33PPPTKZTF6P2267zavNqVOnNGXKFNlsNkVFRWnmzJk6c+ZMOx4FAAAIZgENO2fPntWQIUO0cuXKFtvcdtttOnHihOexdu1ar/VTpkzRwYMHlZeXpw0bNig/P1+zZ8/2d9cBAEAHEdD77IwdO1Zjx449b5vQ0FDFxcU1u+7w4cN67733tHfvXg0fPlyS9Oyzz+r222/X73//e8XHx/u8zwAAoGMJ+psKbt++XTExMerWrZtuvfVWPfbYY+revbskqaCgQFFRUZ6gI0mjR4+W2WzW7t279aMf/ajZmnV1daqrq/M8r66uliQ5nU45nU4/Hk37aDwGIxyLkTFOwcnlcslqtSrEHCKLLJLk+fdShZhDZLVa5XK5GPdvOfe8hyik1du1doxCO4fKarUqtFPoRdVvrY5c399fl/78Wdfamia32+32+d7bwGQy6a233tKECRM8y15//XWFh4crKSlJn3/+uX7zm9+oa9euKigoUKdOnfTEE0/olVdeUXFxsVetmJgYLV68WHPmzGl2Xzk5OVq8eHGT5bm5uQoPD/fpcQEAAP+oqanR5MmTVVVVJZvN1mK7oL6yc9ddd3n+P2jQIA0ePFhXXnmltm/frlGjRrW5bnZ2trKysjzPq6urlZCQoPT09POerI7C6XQqLy9PY8aMkcXim79I4XuMU3Dat2+f0tLSlJKdoujEaM2KmqWXKl+SU5f+V+npktMqWlqk/Px8DRkyxAe9NY5zz3vjx3S0hkWWVo1R+Z5yFa8uVvL8ZMUkx/iiy4ap7++vS3/+rGt8ZeZCgjrsfFvfvn3Vo0cPHTlyRKNGjVJcXJzKy8u92tTX1+vUqVMtzvORvpkHFBoa2mS5xWIx1C8dox2PUTFOwcVsNstut8vhcnh+eTrl9MlnYzlcDtntdpnNZsb8W84972051xcao7r6OtntdtU11Pnlc846cv32+rr0x8+61tbrUPfZ+eqrr3Ty5En17NlTkpSamqrKykoVFhZ62mzbtk0ul0sjRowIVDcBAEAQCeiVnTNnzujIkSOe51988YWKiooUHR2t6OhoLV68WJMmTVJcXJw+//xzPfjgg7rqqquUkZEhSerfv79uu+02zZo1S88//7ycTqfmzp2ru+66i3diAQAASQG+svPhhx9q6NChGjp0qCQpKytLQ4cO1cMPP6xOnTpp//79+uEPf6hrrrlGM2fO1LBhw/SPf/zD6yWo1157Tf369dOoUaN0++2368Ybb9SLL74YqEMCAABBJqBXdm6++Wad781gmzZtumCN6Oho5ebm+rJbAADAQDrUnB0AAICLRdgBAACGRtgBAACGRtgBAACGRtgBAACGRtgBAACG1qE+LgIAjODw4cN+q92jRw8lJib6rT7QERF2AKCdOCodkkmaOnWq3/YRZg1T8SfFBB7gHIQdAGgn9TX1klvqM72Puvft7vP6NcdrdPjFw6qoqCDsAOcg7ABAO7PGWRXRJyLQ3QAuG0xQBgAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhkbYAQAAhhbQsJOfn6/x48crPj5eJpNJb7/9ttd6t9uthx9+WD179pTVatXo0aP12WefebU5deqUpkyZIpvNpqioKM2cOVNnzpxpx6MAAADBLKBh5+zZsxoyZIhWrlzZ7Prly5frmWee0fPPP6/du3erS5cuysjIUG1trafNlClTdPDgQeXl5WnDhg3Kz8/X7Nmz2+sQAABAkOscyJ2PHTtWY8eObXad2+3WihUrtGjRIt1xxx2SpP/5n/9RbGys3n77bd111106fPiw3nvvPe3du1fDhw+XJD377LO6/fbb9fvf/17x8fHtdiwA/k9JSYkqKir8Vr9Hjx5KTEz0W30AxhLQsHM+X3zxhUpLSzV69GjPssjISI0YMUIFBQW66667VFBQoKioKE/QkaTRo0fLbDZr9+7d+tGPftRs7bq6OtXV1XmeV1dXS5KcTqecTqefjqj9NB6DEY7FyIw6Tl999ZWGDR+mWnvthRu3UZg1TIUfFqpXr14+r+1yuWS1WhViDpFFFkny/HupQjuHymq1KrRTqEIU4pOa5woxh8hqtcrlcnW4r6tzz/vFnJvWjpG/z31Hru/vrxt//qxrbU2T2+12+3zvbWAymfTWW29pwoQJkqSdO3fqhhtu0PHjx9WzZ09PuzvvvFMmk0lvvPGGnnjiCb3yyisqLi72qhUTE6PFixdrzpw5ze4rJydHixcvbrI8NzdX4eHhvjsoAADgNzU1NZo8ebKqqqpks9labBe0V3b8KTs7W1lZWZ7n1dXVSkhIUHp6+nlPVkfhdDqVl5enMWPGyGLxzV+k8D2jjtO+ffuUlpamlOwURSRG+Lz+6ZLTKlpapPz8fA0ZMsTn9c/tf3RitGZFzdJLlS/JqUv/q7R8T7mKVxcreX6yYpJjfNBbb/4+N/7U1q8biyytGiN/n/uOXN/fXzf+/FnX+MrMhQRt2ImLi5MklZWVeV3ZKSsrU0pKiqdNeXm513b19fU6deqUZ/vmhIaGKjQ0tMlyi8ViqF86RjseozLaOJnNZtntdjlcDjnk8Hl9h8shu90us9nsl/N2bv8bf3k65fTJsdTV18lut6uuoa5Dnht/utSvmwuNkb/PfUeu315fN/74WdfaekF7n52kpCTFxcVp69atnmXV1dXavXu3UlNTJUmpqamqrKxUYWGhp822bdvkcrk0YsSIdu8zAAAIPgG9snPmzBkdOXLE8/yLL75QUVGRoqOjlZiYqPnz5+uxxx7T1VdfraSkJD300EOKj4/3zOvp37+/brvtNs2aNUvPP/+8nE6n5s6dq7vuuot3YgEAAEkBDjsffvihbrnlFs/zxnk0M2bM0Jo1a/Tggw/q7Nmzmj17tiorK3XjjTfqvffeU1hYmGeb1157TXPnztWoUaNkNps1adIkPfPMM+1+LC3hLbgAAARWQMPOzTffrPO9GcxkMmnJkiVasmRJi22io6OVm5vrj+5dspKSEiX3S/b7W3CLPykm8AAA0IKgnaBsBBUVFaq116r/7P4Kj/f9W9prjtfo8IuHVVFRQdgBAKAFhJ12EB4frog+vn8LLgAAuLCgfTcWAACALxB2AACAoRF2AACAoRF2AACAoRF2AACAoRF2AACAofHWcwBAq/nrrvCHDx/2eU2gEWEHANAq7XFXeIfD958YDhB2DODbfxG5XC5J0r59+2Q2X9orlXz2FoBG/rwr/Mn9J3V0/VHV19f7tC4gEXY6NEelQzJJU6dO9VputVq1du1apaWlyW63X9I++OwtAN/mj7vC1xyv8Wk94FyEnQ6svqZeckt9pvdR977dPctDzCGSpJTsFDlcbb8kzGdvIZj5a44Hc0cA4yHsGIA1zur1V1aIvgk7EYkRcojXv9GUvyaZSv4PCy1d0fT5fpg7AhgGYQe4zLTHJFPJf2GhpSuavmKEuSNc9QK8EXaAy4w/J5lK7RcWvn1F01c68twRrnoBzSPsAJcpf0wylTp2WOjouOoFNI+wAwAGw1UvwBsfFwEAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsAMAAAwtqMNOTk6OTCaT16Nfv36e9bW1tcrMzFT37t3VtWtXTZo0SWVlZQHsMQAACDZBHXYk6dprr9WJEyc8jw8++MCzbsGCBXrnnXe0bt067dixQ8ePH9fEiRMD2FsAABBsOge6AxfSuXNnxcXFNVleVVWll19+Wbm5ubr11lslSatXr1b//v21a9cujRw5sr27CgAAglDQX9n57LPPFB8fr759+2rKlCkqKSmRJBUWFsrpdGr06NGetv369VNiYqIKCgoC1V0AABBkgvrKzogRI7RmzRolJyfrxIkTWrx4sb7//e/r448/VmlpqUJCQhQVFeW1TWxsrEpLS89bt66uTnV1dZ7n1dXVkiSn0ymn0+mz/rtcLlmtVoWYQxSiEJ/VbRTaOVRWq1WhnUK96ltk8fq3rULMIbJarXK5XD49L/hG4zlt73MbqK/LjljfV99LzdXu6OfG1/XbWru1Y9SRz42/6/v7Z70/f9a1tqbJ7Xa7fb53P6msrFTv3r311FNPyWq16t577/UKLZJ0/fXX65ZbbtGTTz7ZYp2cnBwtXry4yfLc3FyFh4f7vN8AAMD3ampqNHnyZFVVVclms7XYLqiv7HxbVFSUrrnmGh05ckRjxoyRw+FQZWWl19WdsrKyZuf4nCs7O1tZWVme59XV1UpISFB6evp5T9bF2rdvn9LS0pSSnaKIxAif1W1UvqdcxauLlTw/WTHJMZ7lFlk0K2qWXqp8SU61PUmfLjmtoqVFys/P15AhQ3zRZZzD6XQqLy9PY8aMkcXimysHrRGor8uOWP87yd/xyfdSc7U7+rnxdf221m7tz7uOfG78Xd/fP+v9+bOu8ZWZC+lQYefMmTP6/PPPNW3aNA0bNkwWi0Vbt27VpEmTJEnFxcUqKSlRamrqeeuEhoYqNDS0yXKLxeLTgTCbzbLb7XK4HHLI4bO6jerq62S321XXUNdsfaecl7Rfh8shu90us9ncrr+MLze+/rq7kEB/XXak+o2/PC/1e6m52h393Pi6/qXWvtAYdeRz4+/67fWz3h8/61pbL6jDzn//939r/Pjx6t27t44fP65HHnlEnTp10t13363IyEjNnDlTWVlZio6Ols1m07x585Samso7sQAAgEdQh52vvvpKd999t06ePKkrrrhCN954o3bt2qUrrrhCkvT000/LbDZr0qRJqqurU0ZGhp577rkA9xoAAASToA47r7/++nnXh4WFaeXKlVq5cmU79QgAAHQ0QR12AABA+zh8+LBf6rpcLr/UvRiEHQAALmOOSodkkqZOneqX+larVWvXrtVXX32lpKQkv+zjQgg7AABcxupr6iW31Gd6H3Xv293n9RvKGyRJJ0+eJOwAAIDAscZZFdHH9/feqjPXXbiRnwX9Z2MBAABcCq7sAEGqpKREFRUVPq/rr0mIABCsCDtAECopKVFyv2TV2mv9tg+Hw/d3eQWAYETYAYJQRUWFau216j+7v8LjffvhtCf3n9TR9UdVX1/v07oAEKwIO0AQC48P9/mEwZrjNT6tBwDBjgnKAADA0Ag7AADA0Ag7AADA0Ag7AADA0Ag7AADA0Hg3Fi7IXzeh69GjhxITE/1SGwCARoQdtMjfn4QbZg1T8SfFBB4AgF8RdtAif34Sbs3xGh1+8bAqKioIOwAAvyLs4IL89Um4AAC0ByYoAwAAQyPsAAAAQyPsAAAAQyPsAAAAQ2OCMgyrpKREFRUVfqvPfYIAoGMg7MCQSkpKlNwvWbX2Wr/tIzQ0VP/v//0/9ezZs03bu1wuSdK+fftkNntfZPXXjRwB4HJE2IEhVVRUqNZeq/6z+ys8Ptzn9as+rdKRtUf0gx/8oM01rFar1q5dq7S0NNnt9mbbOByONtcHAHyDsANDC48P98s9gmqO11zyDRdDzCGSpJTsFDlc3qHm5P6TOrr+qOrr6y+5rwBwuSPsAJfgUm64GKJvwk5EYoQc8g47NcdrLrlvAIBv8G4sAABgaIQdAABgaIQdAABgaIQdAABgaIQdAABgaIQdAABgaIZ56/nKlSv1u9/9TqWlpRoyZIieffZZXX/99YHuFi7AX3cK5g7EAIBGhgg7b7zxhrKysvT8889rxIgRWrFihTIyMlRcXKyYmJhAdw/NcFQ6JJM0depU/+6HOxADwGXPEGHnqaee0qxZs3TvvfdKkp5//nn9/e9/15///Gf9+te/DnDv0Jz6mvpLvgPx+XAHYgBAow4fdhwOhwoLC5Wdne1ZZjabNXr0aBUUFASwZ2iNS7kD8flwB2IAQKMOH3YqKirU0NCg2NhYr+WxsbH65JNPmt2mrq5OdXV1nudVVVWSpFOnTsnpdPqsb9XV1QoLC5PjK4fOOs76rG4jZ4VTYWFhch536mzn/6vvNDtVM6BGZ4+cbfKZS76o7wv+rN1R6p9vnDj3QVI/5KxPvpeard3Rz02QfF229uddRz43/q7v7767TrpUc1WNqqurdfLkSZ/WPn36tCTJ7Xafv6G7g/v666/dktw7d+70Wv7AAw+4r7/++ma3eeSRR9ySePDgwYMHDx4GeBw7duy8WaHDX9np0aOHOnXqpLKyMq/lZWVliouLa3ab7OxsZWVleZ67XC6dOnVK3bt3l8lk8mt/20N1dbUSEhJ07Ngx2Wy2QHcHLWCcgh9jFPwYo+DnzzFyu906ffq04uPjz9uuw4edkJAQDRs2TFu3btWECRMkfRNetm7dqrlz5za7TWhoqEJDQ72WRUVF+bmn7c9ms/HN3wEwTsGPMQp+jFHw89cYRUZGXrBNhw87kpSVlaUZM2Zo+PDhuv7667VixQqdPXvW8+4sAABw+TJE2PnJT36if//733r44YdVWlqqlJQUvffee00mLQMAgMuPIcKOJM2dO7fFl60uN6GhoXrkkUeavFSH4MI4BT/GKPgxRsEvGMbI5HZf6P1aAAAAHRcfBAoAAAyNsAMAAAyNsAMAAAyNsAMAAAyNsNOBLV26VNddd50iIiIUExOjCRMmqLi42KtNbW2tMjMz1b17d3Xt2lWTJk1qcrdptJ9ly5bJZDJp/vz5nmWMUeB9/fXXmjp1qrp37y6r1apBgwbpww8/9Kx3u916+OGH1bNnT1mtVo0ePVqfffZZAHt8eWloaNBDDz2kpKQkWa1WXXnllXr00Ue9Pg+JMWp/+fn5Gj9+vOLj42UymfT22297rW/NmJw6dUpTpkyRzWZTVFSUZs6cqTNnzvi8r4SdDmzHjh3KzMzUrl27lJeXJ6fTqfT0dJ09+38f5LZgwQK98847WrdunXbs2KHjx49r4sSJAez15Wvv3r164YUXNHjwYK/ljFFg/ec//9ENN9wgi8WijRs36tChQ/rDH/6gbt26edosX75czzzzjJ5//nnt3r1bXbp0UUZGhmprawPY88vHk08+qVWrVulPf/qTDh8+rCeffFLLly/Xs88+62nDGLW/s2fPasiQIVq5cmWz61szJlOmTNHBgweVl5enDRs2KD8/X7Nnz/Z9Zy/9ozgRLMrLy92S3Dt27HC73W53ZWWl22KxuNetW+dpc/jwYbckd0FBQaC6eVk6ffq0++qrr3bn5eW5b7rpJvf999/vdrsZo2CwcOFC94033tjiepfL5Y6Li3P/7ne/8yyrrKx0h4aGuteuXdseXbzsjRs3zv3Tn/7Ua9nEiRPdU6ZMcbvdjFEwkOR+6623PM9bMyaHDh1yS3Lv3bvX02bjxo1uk8nk/vrrr33aP67sGEhVVZUkKTo6WpJUWFgop9Op0aNHe9r069dPiYmJKigoCEgfL1eZmZkaN26c11hIjFEw+Nvf/qbhw4frxz/+sWJiYjR06FC99NJLnvVffPGFSktLvcYoMjJSI0aMYIzayfe+9z1t3bpVn376qSRp3759+uCDDzR27FhJjFEwas2YFBQUKCoqSsOHD/e0GT16tMxms3bv3u3T/hjmDsqXO5fLpfnz5+uGG27QwIEDJUmlpaUKCQlp8iGnsbGxKi0tDUAvL0+vv/66/vnPf2rv3r1N1jFGgfevf/1Lq1atUlZWln7zm99o7969+uUvf6mQkBDNmDHDMw7f/vgZxqj9/PrXv1Z1dbX69eunTp06qaGhQY8//rimTJkiSYxREGrNmJSWliomJsZrfefOnRUdHe3zcSPsGERmZqY+/vhjffDBB4HuCs5x7Ngx3X///crLy1NYWFigu4NmuFwuDR8+XE888YQkaejQofr444/1/PPPa8aMGQHuHSTpf//3f/Xaa68pNzdX1157rYqKijR//nzFx8czRmgVXsYygLlz52rDhg16//331atXL8/yuLg4ORwOVVZWerUvKytTXFxcO/fy8lRYWKjy8nJ997vfVefOndW5c2ft2LFDzzzzjDp37qzY2FjGKMB69uypAQMGeC3r37+/SkpKJMkzDt9+hxxj1H4eeOAB/frXv9Zdd92lQYMGadq0aVqwYIGWLl0qiTEKRq0Zk7i4OJWXl3utr6+v16lTp3w+boSdDsztdmvu3Ll66623tG3bNiUlJXmtHzZsmCwWi7Zu3epZVlxcrJKSEqWmprZ3dy9Lo0aN0oEDB1RUVOR5DB8+XFOmTPH8nzEKrBtuuKHJLRs+/fRT9e7dW5KUlJSkuLg4rzGqrq7W7t27GaN2UlNTI7PZ+9dVp06d5HK5JDFGwag1Y5KamqrKykoVFhZ62mzbtk0ul0sjRozwbYd8Ot0Z7WrOnDnuyMhI9/bt290nTpzwPGpqajxtfv7zn7sTExPd27Ztc3/44Yfu1NRUd2pqagB7jXPfjeV2M0aBtmfPHnfnzp3djz/+uPuzzz5zv/baa+7w8HD3q6++6mmzbNkyd1RUlPuvf/2re//+/e477rjDnZSU5Lbb7QHs+eVjxowZ7u985zvuDRs2uL/44gv3+vXr3T169HA/+OCDnjaMUfs7ffq0+6OPPnJ/9NFHbknup556yv3RRx+5v/zyS7fb3boxue2229xDhw5179692/3BBx+4r776avfdd9/t874SdjowSc0+Vq9e7Wljt9vdv/jFL9zdunVzh4eHu3/0ox+5T5w4EbhOo0nYYYwC75133nEPHDjQHRoa6u7Xr5/7xRdf9FrvcrncDz30kDs2NtYdGhrqHjVqlLu4uDhAvb38VFdXu++//353YmKiOywszN23b1/3b3/7W3ddXZ2nDWPU/t5///1mfwfNmDHD7Xa3bkxOnjzpvvvuu91du3Z122w297333us+ffq0z/tqcrvPuQUlAACAwTBnBwAAGBphBwAAGBphBwAAGBphBwAAGBphBwAAGBphBwAAGBphBwAAGBphB0C7ufnmmzV//vyg3G+fPn20YsUKz3OTyaS3337br/0C0D741HMAhrd+/XpZLJaL2ubEiRPq1q2bJOno0aNKSkrSRx99pJSUFD/0EIA/EXYAGF50dPRFb8OnZQPGwctYANqVy+XSgw8+qOjoaMXFxSknJ0fSN1dPTCaTioqKPG0rKytlMpm0fft2SdL27dtlMpm0adMmDR06VFarVbfeeqvKy8u1ceNG9e/fXzabTZMnT1ZNTY2nzrdfxiovL9f48eNltVqVlJSk1157rUk/z30ZKykpSZI0dOhQmUwm3XzzzcrPz5fFYlFpaanXdvPnz9f3v//9Sz9RAHyGsAOgXb3yyivq0qWLdu/ereXLl2vJkiXKy8u7qBo5OTn605/+pJ07d+rYsWO68847tWLFCuXm5urvf/+7Nm/erGeffbbF7e+55x4dO3ZM77//vt58800999xzKi8vb7H9nj17JElbtmzRiRMntH79eqWlpalv3776y1/+4mnndDr12muv6ac//elFHQ8A/yLsAGhXgwcP1iOPPKKrr75a06dP1/Dhw7V169aLqvHYY4/phhtu0NChQzVz5kzt2LFDq1at0tChQ/X9739f//Vf/6X333+/2W0//fRTbdy4US+99JJGjhypYcOG6eWXX5bdbm9xf1dccYUkqXv37oqLi/O8LDZz5kytXr3a0+6dd95RbW2t7rzzzos6HgD+RdgB0K4GDx7s9bxnz57nvapyoRqxsbEKDw9X3759vZa1VPPw4cPq3Lmzhg0b5lnWr18/RUVFXVQfpG+uEB05ckS7du2SJK1Zs0Z33nmnunTpctG1APgPE5QBtKtvvyvKZDLJ5XLJbP7mby+32+1Z53Q6L1jDZDK1WNPfYmJiNH78eK1evVpJSUnauHGjZ34RgODBlR0AQaHxpaITJ054lp07WdlX+vXrp/r6ehUWFnqWFRcXq7KyssVtQkJCJEkNDQ1N1t13331644039OKLL+rKK6/UDTfc4PM+A7g0hB0AQcFqtWrkyJFatmyZDh8+rB07dmjRokU+309ycrJuu+02/exnP9Pu3btVWFio++67T1artcVtYmJiZLVa9d5776msrExVVVWedRkZGbLZbHrsscd07733+ry/AC4dYQdA0Pjzn/+s+vp6DRs2TPPnz9djjz3ml/2sXr1a8fHxuummmzRx4kTNnj1bMTExLbbv3LmznnnmGb3wwguKj4/XHXfc4VlnNpt1zz33qKGhQdOnT/dLfwFcGpP73BfIAQAXbebMmfr3v/+tv/3tb4HuCoBmMEEZANqoqqpKBw4cUG5uLkEHCGKEHQBoozvuuEN79uzRz3/+c40ZMybQ3QHQAl7GAgAAhsYEZQAAYGiEHQAAYGiEHQAAYGiEHQAAYGiEHQAAYGiEHQAAYGiEHQAAYGiEHQAAYGiEHQAAYGj/H+3ysPJVqL9zAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplot(1,1,1)\n",
        "sns.histplot(data['rainfall'],color=\"black\")\n",
        "plt.title(\"Rainfall(mm)\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "-vvRf18rJcAV",
        "outputId": "f7008d43-cbe1-483e-c32a-48745fce5a3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Summer Crops\")\n",
        "print(data[(data['temperature'] > 30) & (data['humidity'] > 50)]['label'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XyF7k7c3J2nF",
        "outputId": "32f25030-472a-4cbf-e6de-ca27c0385829"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summer Crops\n",
            "['pigeonpeas' 'mothbeans' 'blackgram' 'mango' 'grapes' 'orange' 'papaya']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Winter Crops\")\n",
        "print(data[(data['temperature'] < 20) & (data['humidity'] > 30)]['label'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43ETiKAvKAAx",
        "outputId": "de7c65e5-d841-4791-d348-f5dca0cd79e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Winter Crops\n",
            "['maize' 'pigeonpeas' 'lentil' 'pomegranate' 'grapes' 'orange']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Rainy Crops\")\n",
        "print(data[(data['rainfall'] > 200) & (data['humidity'] > 30)]['label'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8oZMRO0KDu9",
        "outputId": "ef452353-a1e0-47fb-ffe8-9bf0364064ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rainy Crops\n",
            "['rice' 'papaya' 'coconut']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Clustering with KMeans"
      ],
      "metadata": {
        "id": "y5Na6jkjLE21"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.loc[:,[\"N\",\"P\",\"K\",\"temperature\",\"ph\",\"humidity\",\"rainfall\"]]\n",
        "x_data=pd.DataFrame(x)"
      ],
      "metadata": {
        "id": "-gqoVb52KPbt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "euQD6cXLJ4s0",
        "outputId": "30005559-d6cd-4095-c536-24599e924e58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    N   P   K  temperature        ph   humidity    rainfall\n",
              "0  90  42  43    20.879744  6.502985  82.002744  202.935536\n",
              "1  85  58  41    21.770462  7.038096  80.319644  226.655537\n",
              "2  60  55  44    23.004459  7.840207  82.320763  263.964248\n",
              "3  74  35  40    26.491096  6.980401  80.158363  242.864034\n",
              "4  78  42  42    20.130175  7.628473  81.604873  262.717340"
            ],
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              "      <th>ph</th>\n",
              "      <th>humidity</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>20.879744</td>\n",
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              "      <td>202.935536</td>\n",
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              "      <td>242.864034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>20.130175</td>\n",
              "      <td>7.628473</td>\n",
              "      <td>81.604873</td>\n",
              "      <td>262.717340</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ca722d9-392d-414b-8e5b-c202b200afa9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"]=(10,4)\n",
        "l=[]\n",
        "for i in range(1,11):\n",
        "  k=KMeans(n_clusters=i,init=\"k-means++\",max_iter=500,n_init=10,random_state=0)\n",
        "  k.fit(x)\n",
        "  l.append(k.inertia_)\n",
        "  print(l)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eUIuP4sLKJ4",
        "outputId": "782c5ae0-cceb-4982-e2b8-d7d04ad2e08e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[18820170.46364591]\n",
            "[18820170.46364591, 12070916.353533484]\n",
            "[18820170.46364591, 12070916.353533484, 7423970.8654109035]\n",
            "[18820170.46364591, 12070916.353533484, 7423970.8654109035, 5337369.950298384]\n",
            "[18820170.46364591, 12070916.353533484, 7423970.8654109035, 5337369.950298384, 4417511.116768206]\n",
            "[18820170.46364591, 12070916.353533484, 7423970.8654109035, 5337369.950298384, 4417511.116768206, 3748646.207999495]\n",
            "[18820170.46364591, 12070916.353533484, 7423970.8654109035, 5337369.950298384, 4417511.116768206, 3748646.207999495, 3122776.0933495294]\n",
            "[18820170.46364591, 12070916.353533484, 7423970.8654109035, 5337369.950298384, 4417511.116768206, 3748646.207999495, 3122776.0933495294, 2727858.0937568424]\n",
            "[18820170.46364591, 12070916.353533484, 7423970.8654109035, 5337369.950298384, 4417511.116768206, 3748646.207999495, 3122776.0933495294, 2727858.0937568424, 2374020.036785368]\n",
            "[18820170.46364591, 12070916.353533484, 7423970.8654109035, 5337369.950298384, 4417511.116768206, 3748646.207999495, 3122776.0933495294, 2727858.0937568424, 2374020.036785368, 2132297.316327442]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,11),l)\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "wItf23_OMUYQ",
        "outputId": "3692cddf-a3c6-4137-9512-2d13401692a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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LtXbtWklSXl6enE6n4uLiahwXFxdX676iatOmTVNhYaF3y87ObuiP4bN6tonUn67oIkma8d4mHSw4bnIiAAAAoHEENPQP6Natm7p16+Z9PXjwYO3atUt///vf9c9//vOczmmz2WSz2S5URL93++Wd9MW2w/pxX4Gmvr1O/7o1VRYLl8sBAADAt5mytPbAgQO1c+dOSVJMTIysVqtyc2uuaJabm6v4+Hgz4vmdAKtFT4/to5BAq77Zla/53+wxOxIAAADQ4EwpQ5mZmUpISJAkBQUFqV+/fsrIyPC+73K5lJGRoUGDBpkRzy8lx4TpvqsvliTNWrpVOw8XmZwIAAAAaFj1vkyuuLjYO6sjSVlZWcrMzFRUVJTatWunadOm6cCBA3rjjTckSbNnz1ZycrJ69OihsrIyzZ07V1988YU+/fRT7znS09M1ceJE9e/fXwMHDtTs2bNVUlKiW2655QJ8RNTVjant9PnmXC3ffkRTFq7Tu38YrEArz+UFAACAb6p3GVq9erWGDRvmfZ2eni5JmjhxoubPn69Dhw5p37593vfLy8t1991368CBAwoNDVXv3r31+eef1zjHuHHjdOTIEU2fPl05OTnq06ePli5dWmtRBTQswzD0+HW9NXz2Cm04UKhnM3Yo/apuZz8QAAAAaIbO6zlDTUV91hLH2X2w/qDufPNHWS2G/vP7QerbrpXZkQAAAIA6aZLPGULz8aveifpNn0Q5XW6lL1qn0vJKsyMBAAAAFxxlCKf0t1/3VHxEsLLySjTzo61mxwEAAAAuOMoQTikyNFBPjkmRJP3z271avv2IyYkAAACAC4syhNO6rEuMbh7cQZL057fXqaC03NxAAAAAwAVEGcIZ/WXERerYOkyHixz665KNZscBAAAALhjKEM4oJMiqv4/tI6vF0AfrD+m9zANmRwIAAAAuCMoQziolqaXu+kVnSdL9SzbqUOFxkxMBAAAA548yhDq5Y1hnpbSNlL2sUvf8Z71crmb/eCoAAAD4OcoQ6iTQatHT4/ooONCir3bk6Z/f7jU7EgAAAHBeKEOos06tW2jayIslSTM/3qJdR4pNTgQAAACcO8oQ6uWmS9traJcYlVW4lL4wUxVOl9mRAAAAgHNCGUK9WCyGHr+utyKCA7Ruf6HmfLnT7EgAAADAOaEMod4SIkP00OiekqRnv9ipddkF5gYCAAAAzgFlCOfk1ymJurp3gpwut6YsytTxcqfZkQAAAIB6oQzhnBiGoUdG91RsuE27j5Ro1tKtZkcCAAAA6oUyhHPWMjRIj1/XW5I0/5s9+mrHEZMTAQAAAHVHGcJ5ubxbrG68tJ0k6c9vr1dhaYXJiQAAAIC6oQzhvN33y4uVHBOmHHuZpr+/0ew4AAAAQJ1QhnDeQoMC9NTYFFkM6b3Mg/pg/UGzIwEAAABnRRnCBXFJu1a6Y1hnSdL/W7xRufYykxMBAAAAZ0YZwgXzxyu6qGebCBUer9A9/1kvt9ttdiQAAADgtChDuGACrRb9fWwfBQVYtHz7Ef37u31mRwIAAABOizKEC6pLXLj+MuIiSdIjH25RVl6JyYkAAACAU6MM4YK7ZXAHDe4UreMVTqUvylSl02V2JAAAAKAWyhAuOIvF0BNjUhRuC9CP+wr04vJdZkcCAAAAaqEMoUG0aRmiB3/TQ5I0+/Md2nig0OREAAAAQE2UITSYa/q20cie8ap0uTVlYabKKpxmRwIAAAC8KENoMIZh6JFreimmhU07DhfriU+2mR0JAAAA8KIMoUFFhQXp8et6SZLmrczSN7vyTE4EAAAAeFCG0OB+cVGcrh/YTpI0ddE62csqTE4EAAAAUIbQSP569cVqHx2qg4VleuD9TWbHAQAAAOpfhlasWKFRo0YpMTFRhmFoyZIlZxz/7rvv6sorr1Tr1q0VERGhQYMG6ZNPPqkx5oEHHpBhGDW2iy66qL7R0ISF2QL09NgUWQzp3bUHtHTjIbMjAQAAwM/VuwyVlJQoJSVFc+bMqdP4FStW6Morr9RHH32kNWvWaNiwYRo1apR+/PHHGuN69OihQ4cOebeVK1fWNxqauH7to/T7n3eSJE17d4MOF5WZnAgAAAD+LKC+B4wcOVIjR46s8/jZs2fXeP3oo4/qvffe03//+1/17dv3RJCAAMXHx9fpnA6HQw6Hw/vabrfXOQ/MNTmtq5ZtO6LNh+y6950NmjexvwzDMDsWAAAA/FCj3zPkcrlUVFSkqKioGvt37NihxMREdezYUTfccIP27dt32nPMnDlTkZGR3i0pKamhY+MCCQqw6O/j+ijIatEXWw9rwQ/ZZkcCAACAn2r0MvTkk0+quLhYY8eO9e5LTU3V/PnztXTpUr3wwgvKysrS0KFDVVRUdMpzTJs2TYWFhd4tO5u/UDcn3eLD9efh3SRJD32wWXvzS0xOBAAAAH/UqGXozTff1IMPPqhFixYpNjbWu3/kyJEaM2aMevfureHDh+ujjz5SQUGBFi1adMrz2Gw2RURE1NjQvNx6WbJSk6NUWu7U3YvWyelymx0JAAAAfqbRytCCBQs0adIkLVq0SGlpaWcc27JlS3Xt2lU7d+5spHRobBaLoafGpqiFLUCr9x7TSyt2mR0JAAAAfqZRytBbb72lW265RW+99Zauvvrqs44vLi7Wrl27lJCQ0AjpYJa2rUI1Y1R3SdLfP9uuTQcLTU4EAAAAf1LvMlRcXKzMzExlZmZKkrKyspSZmeld8GDatGmaMGGCd/ybb76pCRMm6KmnnlJqaqpycnKUk5OjwsITf/GdOnWqli9frj179uibb77RNddcI6vVquuvv/48Px6auuv6tdVV3eNU4XQrfeE6lVU4zY4EAAAAP1HvMrR69Wr17dvXuyx2enq6+vbtq+nTp0uSDh06VGMluJdfflmVlZW64447lJCQ4N3+9Kc/ecfs379f119/vbp166axY8cqOjpa3377rVq3bn2+nw9NnGEYmvnbXoppEaRtuUV66tNtZkcCAACAnzDcbnezv3PdbrcrMjJShYWFLKbQTH22OVe3vbFakvTXqy/WpKEdTU4EAACA5qg+3aDRl9YGTuXK7nH64y86S5Ie/nCL5n612+REAAAA8HWUITQZU67sWqMQvbKCQgQAAICGQxlCk2EYhqcQXdFFkvTIR1v0MktuAwAAoIFQhtCkGIah9Cu76k9VhejRj7bqpeUUIgAAAFx4lCE0SVNOKkQzP96qFylEAAAAuMAoQ2iyplzZVZPTPIXosY+36oVlFCIAAABcOJQhNGmT07pqSlpXSdKspVv1/LKdJicCAACAr6AMocn7U1oXpV/pKUSPL92mOV9SiAAAAHD+KENoFv54RRfdXVWInviEQgQAAIDzRxlCs3HXFV009aoThei5L3aYnAgAAADNGWUIzcqdv+iiPw/vJkl68tPtejaDQgQAAIBzQxlCs3PHsM66Z4SnED312XY9QyECAADAOaAMoVn6w+Wd9ZcRF0mSnv5su/7xOYUIAAAA9UMZQrN1++WddO9ITyH6++fbNfvz7SYnAgAAQHNCGUKz9vufd9K0qkI0+/Md+vtnFCIAAADUDWUIzd7//byT7vulpxD9I4NCBAAAgLqhDMEn/O/POun//fJiSZ5C9PRn2+V2u01OBQAAgKaMMgSfcdvPOuqvV3sK0TNVM0QUIgAAAJwOZQg+ZdLQkwrRFzuZIQIAAMBpUYbgc04uRM9+sVNPfUohAgAAQG2UIfikSUM76v5fdZckPfflTj356TYKEQAAAGqgDMFn3XpZsqZXFaI5X+7SE59QiAAAAHACZQg+7XeXJWvGKE8hen7ZLj1OIQIAAEAVyhB83i1DkvVAVSF6YdkuzVpKIQIAAABlCH7i5iHJevDXPSRJLy7fpceWbqUQAQAA+DnKEPzGxMEd9LffeArRS8t367GPKUQAAAD+jDIEvzJhUAc9VF2IVuzWTAoRAACA36IMwe/cNKiDHhrdU5L08ordevSjLRQiAAAAP0QZgl+66dL2eriqEL3yVZYe+ZBCBAAA4G8oQ/BbN17aXo9c4ylEc1dm6aEPKEQAAAD+hDIEv3ZDans9ek0vSdKrX2fpbx9sphABAAD4iXqXoRUrVmjUqFFKTEyUYRhasmTJWY9ZtmyZLrnkEtlsNnXu3Fnz58+vNWbOnDnq0KGDgoODlZqaqu+//76+0YBz8j+p7byF6LWv91CIAAAA/ES9y1BJSYlSUlI0Z86cOo3PysrS1VdfrWHDhikzM1OTJ0/WpEmT9Mknn3jHLFy4UOnp6ZoxY4bWrl2rlJQUDR8+XIcPH65vPOCc/E9qO8387YlC9OB/KUQAAAC+znCfx9/4DMPQ4sWLNXr06NOO+ctf/qIPP/xQGzdu9O4bP368CgoKtHTpUklSamqqBgwYoOeee06S5HK5lJSUpLvuukv33ntvrXM6HA45HA7va7vdrqSkJBUWFioiIuJcPw6gBd/v073vbpAk3Ty4g2aM6i7DMExOBQAAgLqy2+2KjIysUzdo8HuGVq1apbS0tBr7hg8frlWrVkmSysvLtWbNmhpjLBaL0tLSvGN+aubMmYqMjPRuSUlJDfcB4FfGD2ynWdf2kmFI87/Zowfe38QMEQAAgI9q8DKUk5OjuLi4Gvvi4uJkt9t1/Phx5eXlyel0nnJMTk7OKc85bdo0FRYWerfs7OwGyw//M25AO836bW8ZhvT6qr2aQSECAADwSQFmBzgXNptNNpvN7BjwYWMHJEmG9Jd31uuNVXvldkt/+00PLpkDAADwIQ1ehuLj45Wbm1tjX25uriIiIhQSEiKr1Sqr1XrKMfHx8Q0dDzitsf2TZEi65531+ue3e+WWW3/7dU9ZLBQiAAAAX9Dgl8kNGjRIGRkZNfZ99tlnGjRokCQpKChI/fr1qzHG5XIpIyPDOwYwy5j+SXriuhQZhvSvb/fp/vc2yuXikjkAAABfUO8yVFxcrMzMTGVmZkryLJ2dmZmpffv2SfLczzNhwgTv+N///vfavXu37rnnHm3dulXPP/+8Fi1apClTpnjHpKen65VXXtHrr7+uLVu26Pbbb1dJSYluueWW8/x4wPm7rl9bbyH693f79FcKEQAAgE+o92Vyq1ev1rBhw7yv09PTJUkTJ07U/PnzdejQIW8xkqTk5GR9+OGHmjJliv7xj3+obdu2mjt3roYPH+4dM27cOB05ckTTp09XTk6O+vTpo6VLl9ZaVAEwy3X92sqQNPU/6/Tmd/vkdkuPjOaSOQAAgObsvJ4z1FTUZy1x4Hy8u3a/7n57ndxu6fqBSXpkdC8KEQAAQBPSpJ4zBPiS317SVk+PTZHFkN76Plv3Ld7AJXMAAADNVLNcWhsw0zV928qQofRFmVrwg+cZV49ewwwRAABAc8PMEHAORvdto7+P6yOLIS34IVvT3mWGCAAAoLlhZgg4R7/p00aSNGVhphauzpZbbj32297MEAEAADQTlCHgPJxciBat3i+3W5p1LYUIAACgOeAyOeA8/aZPG80e31cWQ3p7zX7d8856OblkDgAAoMljZgi4AH6dkihD0uSFmfrPGs8M0ePX9ZaVGSIAAIAmi5kh4AIZlZKoZ8b3ldVi6J21+/Xn/6xjhggAAKAJY2YIuICu7p0gSfrjgh/17toDklt6YkwKM0QAAABNEGUIuMCu7p0gw5DueutHvfvjAUkUIgAAgKaIMgQ0gF/2SpChE4XILelJChEAAECTwj1DQAMZ2StBz/1PXwVYDC3+8YDuXpTJPUQAAABNCGUIaEAjeibouf+5RAEWQ0syDyp9UaYqnS6zYwEAAECUIaDBjegZ7y1E72UeVPqidRQiAACAJoAyBDSCET3jNecGTyF6f91BTaEQAQAAmI4yBDSS4T3i9fwNlyjQaui/FCIAAADTUYaARnRVj3g9f0M/byGavJB7iAAAAMxCGQIa2ZXd4/RCVSH6YP0h/YlCBAAAYArKEGCCtJMK0YfrD+lPCzJVQSECAABoVJQhwCRp3eP04o39FGS16MMNh/SnBT9SiAAAABoRZQgw0RUXx+nFmy5RkNWijzbk6I9vUYgAAAAaC2UIMNkvLorTSzd5Zog+3piju96kEAEAADQGyhDQBAy7KNZbiJZuytGdb65VeSWFCAAAoCFRhoAmYthFsXppQj8FBVj0yaZcChEAAEADowwBTciwbrF6+SZPIfp0c67uoBABAAA0GMoQ0MRc3i1Wr0zor6AAiz6jEAEAADQYyhDQBP28a+sahegP/6YQAQAAXGiUIaCJ+nnX1po7ob9sARZ9viVXf/j3GjkqnWbHAgAA8BmUIaAJ+1nX1po7sboQHdYf/rWWQgQAAHCBUIaAJm5ol9aaN3GAbAEWZWw9rNspRAAAABcEZQhoBi7rEuMtRF9QiAAAAC6IcypDc+bMUYcOHRQcHKzU1FR9//33px17+eWXyzCMWtvVV1/tHXPzzTfXen/EiBHnEg3wWZd1idGrNw9QcKCnEP3+n2tUVkEhAgAAOFf1LkMLFy5Uenq6ZsyYobVr1yolJUXDhw/X4cOHTzn+3Xff1aFDh7zbxo0bZbVaNWbMmBrjRowYUWPcW2+9dW6fCPBhQzrH6NWJnkL05bYj+v2/KEQAAADnqt5l6Omnn9Ztt92mW265Rd27d9eLL76o0NBQvfrqq6ccHxUVpfj4eO/22WefKTQ0tFYZstlsNca1atXqtBkcDofsdnuNDfAXg08qRMu2HdH/MUMEAABwTupVhsrLy7VmzRqlpaWdOIHForS0NK1atapO55g3b57Gjx+vsLCwGvuXLVum2NhYdevWTbfffrvy8/NPe46ZM2cqMjLSuyUlJdXnYwDN3uDOJy6ZW779iG57Y7UOFBw3OxYAAECzUq8ylJeXJ6fTqbi4uBr74+LilJOTc9bjv//+e23cuFGTJk2qsX/EiBF64403lJGRoVmzZmn58uUaOXKknM5T/9/uadOmqbCw0LtlZ2fX52MAPmFwpxi9dvNAhQRa9dWOPP3s8S9111s/al12gdnRAAAAmoWAxvxh8+bNU69evTRw4MAa+8ePH+/9vlevXurdu7c6deqkZcuW6Yorrqh1HpvNJpvN1uB5gaZuUKdoLfjfSzVr6VZ9sytf/113UP9dd1ADO0Tp1qHJSrs4TlaLYXZMAACAJqleM0MxMTGyWq3Kzc2tsT83N1fx8fFnPLakpEQLFizQrbfeetaf07FjR8XExGjnzp31iQf4pZSklnrztkv14R8v028vaaNAq6Hv9xzV//1zja54apneWLVHpeWVZscEAABocupVhoKCgtSvXz9lZGR497lcLmVkZGjQoEFnPPbtt9+Ww+HQjTfeeNafs3//fuXn5yshIaE+8QC/1iMxUk+P7aOv7vmFbr+8kyKCA7Qnv1TT39ukQTO/0ONLtyrXXmZ2TAAAgCbDcLvd7vocsHDhQk2cOFEvvfSSBg4cqNmzZ2vRokXaunWr4uLiNGHCBLVp00YzZ86scdzQoUPVpk0bLViwoMb+4uJiPfjgg7r22msVHx+vXbt26Z577lFRUZE2bNhQp8vh7Ha7IiMjVVhYqIiIiPp8HMBnlTgq9Z81+/Xq11nam18qSQq0GhqVkqhJl3VU90T+XQEAAL6nPt2g3vcMjRs3TkeOHNH06dOVk5OjPn36aOnSpd5FFfbt2yeLpeaE07Zt27Ry5Up9+umntc5ntVq1fv16vf766yooKFBiYqKuuuoqPfTQQ9wXBJyHMFuAJg7uoBsvba/PNudq3srd+mHPMb279oDeXXtAQzpHa9LQjvp5l9aycF8RAADwQ/WeGWqKmBkC6iYzu0Bzv9qtjzfmyOny/KvfObaFJl2WrNF92yg40GpyQgAAgPNTn25AGQL80P5jpZr/9R4t+CFbxQ7P4grRYUG6aVB73Xhpe8W0YFYWAAA0T5QhAHVSVFahhT9k67Wv93gf2hoUYNG1l7TRrZclq3NsuMkJAQAA6ocyBKBeKp0ufbwxR3O/2q11+wu9+y/v1lq3De2owZ2iZRjcVwQAAJo+yhCAc+J2u7V67zHN/Wq3Pt2cq+o/HS5OiNCky5I1KiVRQQH1WpEfAACgUVGGAJy3PXkleu3rLC1avV/HK5ySpNhwmyYO7qAbUtupZWiQyQkBAABqowwBuGAKSsv17+/26fVv9uhwkUOSFBJo1Zj+bfW7IcnqEBNmckIAAIATKEMALrjySpf+u+6g5q7M0pZDdkmSYUhXXhynSUM7akCHVtxXBAAATEcZAtBg3G63Vu3K1ytf7daX245496e0jdStQzvqlz3jFWDlviIAAGAOyhCARrHzcJHmrczSO2sPqLzSJUlq0zJENw/uoHEDkxQRHGhyQgAA4G8oQwAaVV6xQ//6dq/+uWqv8kvKJUktbAEaNyBJtwzpoLatQk1OCAAA/AVlCIApyiqcWvLjAc1dmaWdh4slSVaLoRE943Xb0I7qk9TS3IAAAMDnUYYAmMrlcmv5jiOa91WWVu7M8+7v376VJg3tqCu7x8lqYbEFAABw4VGGADQZmw/aNW9llt5fd0AVTs8fN+2jQ/W7Icm6rl9bhdkCTE4IAAB8CWUIQJOTay/T69/s0b+/26fC4xWSpIjgAN1waXtNHNRB8ZHBJicEAAC+gDIEoMkqLa/UO2v2a97KLO3JL5UkBVgM/TolUbcOTVaPxEiTEwIAgOaMMgSgyXO63MrYkqu5X2Xp+z1HvfsHdYzWbT9L1uVdY2XhviIAAFBPlCEAzcq67ALNXZmljzYcktPl+SOpU+sw3XpZR/32kjYKDrSanBAAADQXlCEAzdKBguOa/3WWFnyfrSJHpSQpKixIN17aXjdd2l6tw20mJwQAAE0dZQhAs1ZUVqGFP2Trta/36EDBcUlSUIBF1/Rpo1uHJqtrXLjJCQEAQFNFGQLgEyqdLi3dlKNXvsrSuuwC7/6fd22tSUOTdVnnGBkG9xUBAIATKEMAfIrb7daavcc096ssfbI5R9V/al0UH65bL0vWr/skyhbAfUUAAIAyZHYcAA1ob36JXl2ZpUWr9+t4hVOS1DrcpomD2uuG1PZqFRZkckIAAGAmyhAAn1dYWqE3v9+n+d9kKdfukCQFB1p0Xb+2uvWyjkqOCTM5IQAAMANlCIDfKK906cMNB/XKiixtPmSXJBmGdMVFcbptaLIGJkdxXxEAAH6EMgTA77jdbq3ana+5X2Xpi62Hvft7tYnUpKHJ+mWvBAVaLSYmBAAAjYEyBMCv7TxcrHkrs/Tu2v1yVLokSYmRwbp5SAeNG9BOkSGBJicEAAANhTIEAJLyix3617f79M9v9yivuFySFBZk1dgBSfrdkGQlRYWanBAAAFxolCEAOElZhVPvZR7Q3K+ytONwsSTJYkgjesbr8m6x6tUmUp1jW3AZHQAAPoAyBACn4Ha7tXz7Ec1bmaWvduTVeC8owKKLEyLUMzFCvdpEqmebSHWNC1dQAAUJAIDmhDIEAGex5ZBd767dr/X7C7XpoF3FjspaYwKthrrFh6tXm0j1SIxUrzaR6hYfruBAHvAKAEBTRRkCgHpwudzae7RUGw8UeraDhdqwv1D2stoFKcBiqEtcuHomRqhn1QxS94QIhQRRkAAAaAoavAzNmTNHTzzxhHJycpSSkqJnn31WAwcOPOXY+fPn65Zbbqmxz2azqayszPva7XZrxowZeuWVV1RQUKAhQ4bohRdeUJcuXeqUhzIE4EJzu93af+y4NlQVpOqvx0orao21GFLn2BbqmRh5oiAlRqiFLcCE5AAA+Lf6dIN6/5d64cKFSk9P14svvqjU1FTNnj1bw4cP17Zt2xQbG3vKYyIiIrRt2zbv658+APHxxx/XM888o9dff13Jycm6//77NXz4cG3evFnBwcH1jQgA580wDCVFhSopKlS/7JUgyVOQDhaWnZhBOlCoDQfsyit2aHtusbbnFuvdHw9UHS8lx4R57j+qKkk92kQoIphlvQEAaCrqPTOUmpqqAQMG6LnnnpMkuVwuJSUl6a677tK9995ba/z8+fM1efJkFRQUnPJ8brdbiYmJuvvuuzV16lRJUmFhoeLi4jR//nyNHz++1jEOh0MOh8P72m63KykpiZkhAKbItZedNHtk18YDhcqxl51ybIfoUPVoE3lSSYpQy9CgRk4MAIDvarCZofLycq1Zs0bTpk3z7rNYLEpLS9OqVatOe1xxcbHat28vl8ulSy65RI8++qh69OghScrKylJOTo7S0tK84yMjI5WamqpVq1adsgzNnDlTDz74YH2iA0CDiYsIVlxEsK64OM6770iRQxsPFmrTSSXpQMFx7ckv1Z78Un24/pB3bNtWIeqZGKlebSPVo2o1u+gWNjM+CgAAfqVeZSgvL09Op1NxcXE19sfFxWnr1q2nPKZbt2569dVX1bt3bxUWFurJJ5/U4MGDtWnTJrVt21Y5OTnec/z0nNXv/dS0adOUnp7ufV09MwQATUXrcJuGdYvVsG4nLh8+WlKuTQc95WjTAbs2HizU3vxS7T92XPuPHdfSTSf+zEuIDPbcf5QYqV5tI9QzMVKxEVw2DADAhdTgd/cOGjRIgwYN8r4ePHiwLr74Yr300kt66KGHzumcNptNNhv/1xRA8xIVFqShXVpraJfW3n2FpRXadKj6HiTPJXa780p0qLBMhwrL9NnmXO/Y2HCbd4GGnokR6tU2UvERwbXuwwQAAHVTrzIUExMjq9Wq3NzcGvtzc3MVHx9fp3MEBgaqb9++2rlzpyR5j8vNzVVCQkKNc/bp06c+8QCg2YkMDdTgTjEa3CnGu6+orEKbD9q18aDdu1DDriPFOlzk0BdbD+uLrYe9Y6PDgqoKUoT3eUhtW4VQkAAAqIN6laGgoCD169dPGRkZGj16tCTPAgoZGRm6884763QOp9OpDRs26Je//KUkKTk5WfHx8crIyPCWH7vdru+++0633357feIBgE8IDw5UasdopXaM9u4rLa/UlkN2bdhf6C1JOw4XK7+kXMu3H9Hy7Ue8Y1uGBqpnomf1uuqFGtpHh1KQAAD4iXpfJpeenq6JEyeqf//+GjhwoGbPnq2SkhLvs4QmTJigNm3aaObMmZKkv/3tb7r00kvVuXNnFRQU6IknntDevXs1adIkSZ7laydPnqyHH35YXbp08S6tnZiY6C1cAODvQoMC1K99lPq1j/LuK6twamtOUdU9SJ57kbbnFqmgtEIrd+Zp5c4879jw4ADv4gzVl9olR4fJYqEgAQD8V73L0Lhx43TkyBFNnz5dOTk56tOnj5YuXepdAGHfvn2yWCze8ceOHdNtt92mnJwctWrVSv369dM333yj7t27e8fcc889Kikp0f/+7/+qoKBAl112mZYuXcozhgDgDIIDreqT1FJ9klp69zkqndqeU6yN3oUaCrUlp0hFZZX6dvdRfbv7qHdsWJBVPU6eQWoTqU6tW8hKQQIA+Il6P2eoKarPWuIA4G8qnC7tyC323H9UVZK2HLKrrMJVa2xIoFUXJ4R77j+qeh5S59gWCrRaTnFmAACanvp0A8oQAPihSqdLu/NKqu5B8izSsOmgXaXlzlpjgwIsujg+3Ht5XXVBCg60mpAcAIAzowwBAOrN6XIrK6/E8yykqpK06YBdRY7KWmMNQ0qICFaHmDDPFh2qDtFhSo4JU1JUKEUJAGAayhAA4IJwudzad7T0pHuQPA+LLSitOO0xhiElRoaoQ0yo2keHKTnaU5iSY0KVFBUqWwBFCQDQcChDAIAG43a7dbSkXHvyS7Qnr1R78kuUlVeivfml2pNXcsqZpGrVRSk5Jkzto0OVHBOmDtFh6kBRAgBcIJQhAIAp3G638kvKtTe/RFl5nnKUlV+ivVXFqfgMRcliSIktTxSl6svu2keHqV1UqIICWMQBAHB2lCEAQJPjdruVV1xdlEo8M0tVs0l78kpUcorFG6pZDKlNqxDPLNJJl921jw5TUiuKEgDgBMoQAKBZcbvdOlLs0N78Uk9Rqrrsrro0nWqVu2oWQ2rbKrTWZXcdoj2LObAsOAD4F8oQAMBnuN1uHSlyeGeRqi+7y8or1d6zFCWrxVDbViFVCzmEVq1855lZatsqhKIEAD6IMgQA8AvVRemnl91VL+hwvOLsRan63qQO0aFqH+NZ/a5tqxAFUJQAoFmiDAEA/J7b7dbh6qKUd9L9SfmerazCddpjA6qLUtVM0smr37VpSVECgKasPt0goJEyAQDQqAzDUFxEsOIignVpx+ga77ndbuXaT55RqipMVUuFOypdnvKUXyrpSI1jAyyGkqJCPQ+aPemyu+ToMCW2DKYoAUAzwswQAAAncbncyi0qq/HspOrStDe/VI7K088oBVoNJbU6uSSdWCI8sWWIrBajET8JAPgnZoYAADhHFouhhMgQJUSGaHCnmu+5XG7l2MtOXHbnfeCs53V5pUu780q0O6+k1nmri1K76FC1izppq3odGsR/kgGgsfEnLwAAdWSxGEpsGaLEliEa3Lnmey6XW4fsZdpbteKdZ0bJs+Ld3qNnLkqSFNPCpnZRnpXvkqJC1f6kohQbbpNhMKsEABcal8kBANDAnC63DhUe1778Uu07Wqq9Rz1fs4+Wam9+qQqPV5zx+OBAi2dWKarmzFL76FC1bRWq4EBrI30SAGj6uEwOAIAmxLOMt6e4DD7F+4WlFco+5ilG+45WbyXad7RUBwvKVFbh0o7DxdpxuPiU54+PCFa7qFDPjFJVWar+PjosiFklADgNZoYAAGjCKpwuHSw47plRyvfMJlV/v+9oqYodlWc8PjTIesp7lNpFhapNqxDZAphVAuBbeM4QAAB+wO12q6C04ieX3ZVUfX9cBwuP60z/lTcMKTEyRElRIVWX3XnuV2pXdc9Sy9BAZpUANDuUIQAAIEelUweOHT9x6V2Ny/BKVVruPOPx4baAmqvfnfR9YssQBfJMJQBNEPcMAQAA2QKs6ti6hTq2blHrPbfbrfyScu+ld9WX3WUfLdXeoyXKtTtU5KjUpoN2bTpor3W81WIosWVwVTkKq7GoQ1JUqCJDAhvjIwLAeWFmCAAA1FJW4dT+ny7qcNL3Z3r4rCRFhgR6i1H1ZXfVs0sJkTyAFkDD4TI5AADQYFwut44UO7wFae/Rmgs75BU7znh8oNVQm5YhahcdpnZV9yt5Z5eiQ9XCxoUrAM4dl8kBAIAGY7EYiosIVlxEsAZ0iKr1fml5pbKPHvcu5nDytv/ocZU7XdqTX6o9+aWnPH90WNCJGaXoULVtFaLW4TZFh9kUFRakmBY2hQSxCh6A80cZAgAAF1RoUIC6xYerW3x4rfecLrdy7WW1Lrurnl06WlKu/KotM7vgDD/DqqiwIEW3sCk6LMizVX/foub3UWFBLCEO4JQoQwAAoNF4Fl4IUWLLEF3aMbrW+0VlFd6FHKovuztQcFz5xeXKL3Yor6Rc5ZUulZY7VVp+XPuPHa/Tzw23BXhLkmd2Kcg70xTdwjPbVP19VGiQAlgpD/ALlCEAANBkhAcHqkdipHokRp7yfbfbrZJyp/KLHZ4ZpKqS5P2+xKGjJeXKq9p/tKRclS63ihyVKnJUnvbSvJ9qGRpYe7YpzPaTr573W4YEysKCEECzRBkCAADNhmEYamELUAtbgNpHh511vNvtlv14pfKqSlJ+sUN5xeUnvi8p19GqEpVfXK5jpeVyuaWC0goVlFZo15GSs/4MiyHPrFJVSaq+ryk6LEhRVeUppmp/dAubIoIDeJgt0ERQhgAAgM8yDEORoYGKDA1Up9ZnH+90uVVQWl5jpsnztWoGqqpI5VXtLzxeIZdbyiv2zEYp9+w/I9Bq1ChP1TNMNS7faxGkmKr3Q4OslCeggVCGAAAAqlgthufSuBY2Ke7s4yucLh2ruizPs/iDo8YlenknXbqXX1yuYkelKpxu5dodyrWfeQnyarYAi2emqXp26SczTScXquiwIAUHslgEUFeUIQAAgHMUaLUoNiJYsRHBdRpfVuH0FqO8EkeNS/S8s08nzUqVVbjkqHTpQMFxHSio22IRYUFWb0mKCg1Sy9AgtQoNVKuwILUKDVJUWGDVviC1CgtUq9AgBbJgBPzUOZWhOXPm6IknnlBOTo5SUlL07LPPauDAgacc+8orr+iNN97Qxo0bJUn9+vXTo48+WmP8zTffrNdff73GccOHD9fSpUvPJR4AAECTFBxo9a6mdzZut1ul5c6qGabal+j9dH9+iUMVTs8CEyVVq/HVVbgtQC3DAk9ZnjxfT3qvqkAxAwVfUO8ytHDhQqWnp+vFF19UamqqZs+ereHDh2vbtm2KjY2tNX7ZsmW6/vrrNXjwYAUHB2vWrFm66qqrtGnTJrVp08Y7bsSIEXrttde8r2022zl+JAAAgObPMAyF2QIUZgtQUlToWce73W7ZyyprLBRRUFquo6XlKiit0NESz+tjpRU6VuJZLKLgeIXcbnlX28s+WrfZJ0kKCfQ866llaGDV16oS9dMydVKB4v4nNDWG2+121+eA1NRUDRgwQM8995wkyeVyKSkpSXfddZfuvffesx7vdDrVqlUrPffcc5owYYIkz8xQQUGBlixZUv9PIMlutysyMlKFhYWKiIg4p3MAAAD4G6fLLfvxCh0r9ZSjYyUVVeWpXEdLKqrKk2e/d0xphZyuev310SsowHJSYTpRkk6egTpRojzfh9tYfQ/1U59uUK+ZofLycq1Zs0bTpk3z7rNYLEpLS9OqVavqdI7S0lJVVFQoKiqqxv5ly5YpNjZWrVq10i9+8Qs9/PDDio6u/TA2SXI4HHI4Ttx0aLfb6/MxAAAAIM+CEa3CPEWkrtxuz3ObPLNLJ2aZPDNPNYvVyd+XO10qr3TVa/EISQqwGDVnnaoLVFjQaYtVJM9+Qh3Vqwzl5eXJ6XQqLq7m8ipxcXHaunVrnc7xl7/8RYmJiUpLS/PuGzFihH77298qOTlZu3bt0n333aeRI0dq1apVslprX486c+ZMPfjgg/WJDgAAgAvAMAxFBAcqIjhQ7U/9/61rqb7/qXZJqipUP718r+qyvuMVTlW63MordiivuO4FymJIkSE/mWXyFijP65ahnhX5qmegWoYEKoCFJPxOo64m99hjj2nBggVatmyZgoNPrLoyfvx47/e9evVS79691alTJy1btkxXXHFFrfNMmzZN6enp3td2u11JSUkNGx4AAADn5OT7n9q2qvtxZRUnClT1/U8nl6bqMuW9N6qkQkWOSrncqipZFZLO/uDcahHBAWoVFqSI4EC1qMrbwmb1fA0OUIuggBPfn/R+C1ugwmxW7z5W52s+6lWGYmJiZLValZtb84liubm5io+PP+OxTz75pB577DF9/vnn6t279xnHduzYUTExMdq5c+cpy5DNZmOBBQAAAB8XHGhVQmSIEiLPvvpetfJKlwqO11w04mjVbNTJ90KdvLBE4fEKSZK9rFL2ssrzzm0LsHiLUZgtQOG2AIVVlarw4ACFVZeqqmLlLV1BPy1aAbIFWLhnqgHVqwwFBQWpX79+ysjI0OjRoyV5FlDIyMjQnXfeedrjHn/8cT3yyCP65JNP1L9//7P+nP379ys/P18JCQn1iQcAAAA/FxRgUWx4sGLD6/bsJ8mzkEThcU8xOlZarqKyChU7nCpxVKq4rFLFjkqVOCpVUl6porKq7x1OFVXvd3jGOCpdkiRHpUuOSs+zo85XgMXwFqOTZ59a2GqWphMzVlWlqkbR8nwNDbRyL9VP1PsyufT0dE2cOFH9+/fXwIEDNXv2bJWUlOiWW26RJE2YMEFt2rTRzJkzJUmzZs3S9OnT9eabb6pDhw7KycmRJLVo0UItWrRQcXGxHnzwQV177bWKj4/Xrl27dM8996hz584aPnz4BfyoAAAAQG1Wi6GoMM89ROejwunyFqMSh1PFjpNKVVWxKnFUqri88qSi5fQWrZrFyylJqqwqatWzV+fDMFQ1K2U9acYqoNb3LX5SumpdGlh1Dl+4x6reZWjcuHE6cuSIpk+frpycHPXp00dLly71Lqqwb98+WSwn/sG88MILKi8v13XXXVfjPDNmzNADDzwgq9Wq9evX6/XXX1dBQYESExN11VVX6aGHHuJSOAAAADQbgVaLWlY9nPZ8uVxulZRXl6oTJenkr97i5HCemLEq/0nxqipWTpdbbre8x0l1X5DidIIDLbVK0z3Du6l/h6izH9xE1Ps5Q00RzxkCAAAATs3tdquswnXKIlXz+5MuDSw/cQlgUVlljWJWXnU54Km8/ruB+nnX1o346WprsOcMAQAAAGheDMNQSJBVIUFWtQ4//yuvyitPuhywxuV9TnVPaF4TE5QhAAAAAHUWFGBRUED9HtbbVDX/u54AAAAA4BxQhgAAAAD4JcoQAAAAAL9EGQIAAADglyhDAAAAAPwSZQgAAACAX6IMAQAAAPBLlCEAAAAAfokyBAAAAMAvUYYAAAAA+KUAswNcCG63W5Jkt9tNTgIAAADATNWdoLojnIlPlKGioiJJUlJSkslJAAAAADQFRUVFioyMPOMYw12XytTEuVwuHTx4UOHh4TIMw+w4OEd2u11JSUnKzs5WRESE2XHgB/idQ2Pi9w2Njd85NKam9PvmdrtVVFSkxMREWSxnvivIJ2aGLBaL2rZta3YMXCARERGm/0sE/8LvHBoTv29obPzOoTE1ld+3s80IVWMBBQAAAAB+iTIEAAAAwC9RhtBk2Gw2zZgxQzabzewo8BP8zqEx8fuGxsbvHBpTc/1984kFFAAAAACgvpgZAgAAAOCXKEMAAAAA/BJlCAAAAIBfogwBAAAA8EuUIQAAAAB+iTIE082cOVMDBgxQeHi4YmNjNXr0aG3bts3sWPATjz32mAzD0OTJk82OAh924MAB3XjjjYqOjlZISIh69eql1atXmx0LPsjpdOr+++9XcnKyQkJC1KlTJz300ENi8WBcKCtWrNCoUaOUmJgowzC0ZMmSGu+73W5Nnz5dCQkJCgkJUVpamnbs2GFO2DqgDMF0y5cv1x133KFvv/1Wn332mSoqKnTVVVeppKTE7GjwcT/88INeeukl9e7d2+wo8GHHjh3TkCFDFBgYqI8//libN2/WU089pVatWpkdDT5o1qxZeuGFF/Tcc89py5YtmjVrlh5//HE9++yzZkeDjygpKVFKSormzJlzyvcff/xxPfPMM3rxxRf13XffKSwsTMOHD1dZWVkjJ60bnjOEJufIkSOKjY3V8uXL9bOf/czsOPBRxcXFuuSSS/T888/r4YcfVp8+fTR79myzY8EH3Xvvvfr666/11VdfmR0FfuBXv/qV4uLiNG/ePO++a6+9ViEhIfrXv/5lYjL4IsMwtHjxYo0ePVqSZ1YoMTFRd999t6ZOnSpJKiwsVFxcnObPn6/x48ebmPbUmBlCk1NYWChJioqKMjkJfNkdd9yhq6++WmlpaWZHgY97//331b9/f40ZM0axsbHq27evXnnlFbNjwUcNHjxYGRkZ2r59uyRp3bp1WrlypUaOHGlyMviDrKws5eTk1Phva2RkpFJTU7Vq1SoTk51egNkBgJO5XC5NnjxZQ4YMUc+ePc2OAx+1YMECrV27Vj/88IPZUeAHdu/erRdeeEHp6em677779MMPP+iPf/yjgoKCNHHiRLPjwcfce++9stvtuuiii2S1WuV0OvXII4/ohhtuMDsa/EBOTo4kKS4ursb+uLg473tNDWUITcodd9yhjRs3auXKlWZHgY/Kzs7Wn/70J3322WcKDg42Ow78gMvlUv/+/fXoo49Kkvr27auNGzfqxRdfpAzhglu0aJH+/e9/680331SPHj2UmZmpyZMnKzExkd834BS4TA5Nxp133qkPPvhAX375pdq2bWt2HPioNWvW6PDhw7rkkksUEBCggIAALV++XM8884wCAgLkdDrNjggfk5CQoO7du9fYd/HFF2vfvn0mJYIv+/Of/6x7771X48ePV69evXTTTTdpypQpmjlzptnR4Afi4+MlSbm5uTX25+bmet9raihDMJ3b7dadd96pxYsX64svvlBycrLZkeDDrrjiCm3YsEGZmZnerX///rrhhhuUmZkpq9VqdkT4mCFDhtR6XMD27dvVvn17kxLBl5WWlspiqfnXO6vVKpfLZVIi+JPk5GTFx8crIyPDu89ut+u7777ToEGDTEx2elwmB9PdcccdevPNN/Xee+8pPDzce01pZGSkQkJCTE4HXxMeHl7rfrSwsDBFR0dznxoaxJQpUzR48GA9+uijGjt2rL7//nu9/PLLevnll82OBh80atQoPfLII2rXrp169OihH3/8UU8//bR+97vfmR0NPqK4uFg7d+70vs7KylJmZqaioqLUrl07TZ48WQ8//LC6dOmi5ORk3X///UpMTPSuONfUsLQ2TGcYxin3v/baa7r55psbNwz80uWXX87S2mhQH3zwgaZNm6YdO3YoOTlZ6enpuu2228yOBR9UVFSk+++/X4sXL9bhw4eVmJio66+/XtOnT1dQUJDZ8eADli1bpmHDhtXaP3HiRM2fP19ut1szZszQyy+/rIKCAl122WV6/vnn1bVrVxPSnh1lCAAAAIBf4p4hAAAAAH6JMgQAAADAL1GGAAAAAPglyhAAAAAAv0QZAgAAAOCXKEMAAAAA/BJlCAAAAIBfogwBAAAA8EuUIQAAAAB+iTIEAAAAwC9RhgAAAAD4pf8Puw2iJL23U9cAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "k = KMeans(n_clusters = 4, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
        "y_means = k.fit_predict(x)"
      ],
      "metadata": {
        "id": "otQuYUGwMo65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = data['label']\n",
        "y_means = pd.DataFrame(y_means)\n",
        "z = pd.concat([y_means, a], axis = 1)\n",
        "z = z.rename(columns = {0: 'cluster'})"
      ],
      "metadata": {
        "id": "Nh4BOVu8M-oM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counts = z[z['cluster'] == 0]['label'].value_counts()\n",
        "d = z.loc[z['label'].isin(counts.index[counts >= 50])]\n",
        "d = d['label'].value_counts()\n",
        "print(\"Cluster 1:\", list(d.index))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7i1-5I-SNuhx",
        "outputId": "badf9472-88c1-4388-c5c1-18da1dce57e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster 1: ['chickpea', 'kidneybeans', 'mothbeans', 'mungbean', 'blackgram', 'lentil', 'pomegranate', 'mango', 'orange']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts = z[z['cluster'] == 1]['label'].value_counts()\n",
        "d = z.loc[z['label'].isin(counts.index[counts >= 50])]\n",
        "d = d['label'].value_counts()\n",
        "print(\"Cluster 2:\", list(d.index))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZiXAv0AnN3Ex",
        "outputId": "3dbb0c6b-f3cf-4402-9df1-4d1e4eb85df5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Crops in Cluster 2: ['maize', 'banana', 'watermelon', 'muskmelon', 'cotton']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts = z[z['cluster'] == 2]['label'].value_counts()\n",
        "d = z.loc[z['label'].isin(counts.index[counts >= 50])]\n",
        "d = d['label'].value_counts()\n",
        "print(\"Cluster 3:\", list(d.index))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Q8DKwa9N_rs",
        "outputId": "18b19021-3e87-4a86-b5d2-30a63e1b87fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster 3: ['grapes', 'apple']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts = z[z['cluster'] == 3]['label'].value_counts()\n",
        "d = z.loc[z['label'].isin(counts.index[counts >= 50])]\n",
        "d = d['label'].value_counts()\n",
        "print(\"Cluster 4:\", list(d.index))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcDqyY9zN9H-",
        "outputId": "ad1fff81-c53c-4da3-d4ee-f1dc65dd68d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster 4: ['rice', 'pigeonpeas', 'papaya', 'coconut', 'jute', 'coffee']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Logistic Regression"
      ],
      "metadata": {
        "id": "Fjdn5INhO_7S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y=data['label']\n",
        "x=data.drop([\"label\"],axis=1)\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)"
      ],
      "metadata": {
        "id": "5FNKoMoyPFOh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LogisticRegression()\n",
        "lr.fit(x_train,y_train)\n",
        "y_pred=lr.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3SatvMPQSWu",
        "outputId": "62800197-b838-42b4-a3f5-2d5b36fd80b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cr = classification_report(y_test, y_pred)\n",
        "print(cr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1Xs3FyMQ1rV",
        "outputId": "c644bbf4-642d-417f-f29d-85436a5c11d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       apple       1.00      1.00      1.00        28\n",
            "      banana       1.00      1.00      1.00        30\n",
            "   blackgram       0.84      0.87      0.86        31\n",
            "    chickpea       1.00      1.00      1.00        34\n",
            "     coconut       1.00      1.00      1.00        26\n",
            "      coffee       1.00      1.00      1.00        29\n",
            "      cotton       0.84      0.96      0.90        28\n",
            "      grapes       1.00      1.00      1.00        30\n",
            "        jute       0.83      0.94      0.88        31\n",
            " kidneybeans       0.96      1.00      0.98        26\n",
            "      lentil       0.91      0.91      0.91        22\n",
            "       maize       0.92      0.81      0.86        27\n",
            "       mango       1.00      1.00      1.00        28\n",
            "   mothbeans       0.81      0.81      0.81        36\n",
            "    mungbean       1.00      0.97      0.98        29\n",
            "   muskmelon       1.00      1.00      1.00        30\n",
            "      orange       1.00      1.00      1.00        34\n",
            "      papaya       1.00      0.87      0.93        39\n",
            "  pigeonpeas       0.90      0.96      0.93        28\n",
            " pomegranate       1.00      1.00      1.00        32\n",
            "        rice       0.94      0.86      0.90        37\n",
            "  watermelon       1.00      1.00      1.00        25\n",
            "\n",
            "    accuracy                           0.95       660\n",
            "   macro avg       0.95      0.95      0.95       660\n",
            "weighted avg       0.95      0.95      0.95       660\n",
            "\n"
          ]
        }
      ]
    }
  ]
}